A program of tightly regulated gene expression is at the heart of development for every living organism. Recent years have seen an increased appreciation for the complexity of transcriptional control by gene Transcription Factors (TFs) as well as post-transcriptional control by small RNAs known as microRNAs (miRNAs). The interaction between TFs and miRNAs has been hypothesized to enable established cell types to maintain their identify by silencing leaky transcription, to induce rapid developmental phase transitions via direct negative feedback loops, and many other life-critical tasks. This opens a new challenge to build models that integrate these two mechanisms in order to understand their relative contributions to gene circuit function. The Arabidopsis thaliana model plant system is uniquely amenable for the prediction, validation, and manipulation of functional miRNA and TF interactions within a large eukaryotic gene system. The simplicity of miRNA:target gene base pairing, spatio-temporal organization of cells within the root system, and ease of transgenic line creation make plants an ideal starting point for studying the interplay between TFs and miRNAs in specific gene circuits. The overall goal of the proposed research is to understand how transcription and small RNA-based gene regulation work together in living cells by identifying small TF-miRNA circuits and querying their biological function. A multi-disciplinary team of mentors (Philip Benfey, Uwe Ohler) and collaborators at Duke University will guide the transition to independence through this research. A team of experts in experimental plant biology, systems biology, statistical analysis, and large-scale computing methods will provide training and monitor development during the mentored phase of the research. The specific aims of the research project are: (1) To experimentally map transcriptional control regions in Arabidopsis (2) To identify TF-miRNA gene circuits using computational analysis (3) To characterize the functions encoded by these gene circuits using a combination of experimental and computational methods. The expected impact of the proposed research will be to provide much-needed data and computational methods for the analysis of regulatory networks involving both transcription factors and miRNAs.